Oasis Infobyte : Data Science Internship

Task 2 : Unemployment Analysis with Python

Name of Intern: Pawar Pooja Audumbar

Batch -April Phase 2 OIBSIP

Loading Packages and Data

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data1=pd.read_csv("C:\\Users\\HP-PC\\Desktop\\Mayu\\Unemployment in India.csv")
data1

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
0	Andhra Pradesh	31-05-2019	Monthly	3.65	11999139.0	43.24	Rural
1	Andhra Pradesh	30-06-2019	Monthly	3.05	11755881.0	42.05	Rural
2	Andhra Pradesh	31-07-2019	Monthly	3.75	12086707.0	43.50	Rural
3	Andhra Pradesh	31-08-2019	Monthly	3.32	12285693.0	43.97	Rural
4	Andhra Pradesh	30-09-2019	Monthly	5.17	12256762.0	44.68	Rural
...	...	...	...	...	...	...	...
763	NaN	NaN	NaN	NaN	NaN	NaN	NaN
764	NaN	NaN	NaN	NaN	NaN	NaN	NaN
765	NaN	NaN	NaN	NaN	NaN	NaN	NaN
766	NaN	NaN	NaN	NaN	NaN	NaN	NaN
767	NaN	NaN	NaN	NaN	NaN	NaN	NaN

768 rows × 7 columns

data2=pd.read_csv("C:\\Users\\HP-PC\\Desktop\\Mayu\\Unemployment_Rate_upto_11_2020.csv")
data2

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Region.1	longitude	latitude
0	Andhra Pradesh	31-01-2020	M	5.48	16635535	41.02	South	15.9129	79.740
1	Andhra Pradesh	29-02-2020	M	5.83	16545652	40.90	South	15.9129	79.740
2	Andhra Pradesh	31-03-2020	M	5.79	15881197	39.18	South	15.9129	79.740
3	Andhra Pradesh	30-04-2020	M	20.51	11336911	33.10	South	15.9129	79.740
4	Andhra Pradesh	31-05-2020	M	17.43	12988845	36.46	South	15.9129	79.740
...	...	...	...	...	...	...	...	...	...
262	West Bengal	30-06-2020	M	7.29	30726310	40.39	East	22.9868	87.855
263	West Bengal	31-07-2020	M	6.83	35372506	46.17	East	22.9868	87.855
264	West Bengal	31-08-2020	M	14.87	33298644	47.48	East	22.9868	87.855
265	West Bengal	30-09-2020	M	9.35	35707239	47.73	East	22.9868	87.855
266	West Bengal	31-10-2020	M	9.98	33962549	45.63	East	22.9868	87.855

267 rows × 9 columns

# Store the lengths
data1_len=len(data1)
data1_len

768

data2_len=len(data2)
data2_len

267

data2.head()

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Region.1	longitude	latitude
0	Andhra Pradesh	31-01-2020	M	5.48	16635535	41.02	South	15.9129	79.74
1	Andhra Pradesh	29-02-2020	M	5.83	16545652	40.90	South	15.9129	79.74
2	Andhra Pradesh	31-03-2020	M	5.79	15881197	39.18	South	15.9129	79.74
3	Andhra Pradesh	30-04-2020	M	20.51	11336911	33.10	South	15.9129	79.74
4	Andhra Pradesh	31-05-2020	M	17.43	12988845	36.46	South	15.9129	79.74

data2.tail()

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Region.1	longitude	latitude
262	West Bengal	30-06-2020	M	7.29	30726310	40.39	East	22.9868	87.855
263	West Bengal	31-07-2020	M	6.83	35372506	46.17	East	22.9868	87.855
264	West Bengal	31-08-2020	M	14.87	33298644	47.48	East	22.9868	87.855
265	West Bengal	30-09-2020	M	9.35	35707239	47.73	East	22.9868	87.855
266	West Bengal	31-10-2020	M	9.98	33962549	45.63	East	22.9868	87.855

data2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 267 entries, 0 to 266
Data columns (total 9 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    267 non-null    object 
 1    Date                                     267 non-null    object 
 2    Frequency                                267 non-null    object 
 3    Estimated Unemployment Rate (%)          267 non-null    float64
 4    Estimated Employed                       267 non-null    int64  
 5    Estimated Labour Participation Rate (%)  267 non-null    float64
 6   Region.1                                  267 non-null    object 
 7   longitude                                 267 non-null    float64
 8   latitude                                  267 non-null    float64
dtypes: float64(4), int64(1), object(4)
memory usage: 18.9+ KB

data2.shape

(267, 9)

data2.isnull().sum()

Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Region.1                                    0
longitude                                   0
latitude                                    0
dtype: int64

data2.describe()

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	longitude	latitude
count	267.000000	2.670000e+02	267.000000	267.000000	267.000000
mean	12.236929	1.396211e+07	41.681573	22.826048	80.532425
std	10.803283	1.336632e+07	7.845419	6.270731	5.831738
min	0.500000	1.175420e+05	16.770000	10.850500	71.192400
25%	4.845000	2.838930e+06	37.265000	18.112400	76.085600
50%	9.650000	9.732417e+06	40.390000	23.610200	79.019300
75%	16.755000	2.187869e+07	44.055000	27.278400	85.279900
max	75.850000	5.943376e+07	69.690000	33.778200	92.937600

Exploratory Data Analysis

# Region
colors=sns.color_palette('pastel')
labels=data2['Region'].dropna().unique()
plt.figure(figsize=(18,10))
plt.subplot(1,2,1)
plt.title('Region_Percentage')
plt.pie(data2['Region'].value_counts(),labels=labels,colors=colors,autopct='%.2f%%')

([<matplotlib.patches.Wedge at 0x18eecdac850>,
  <matplotlib.patches.Wedge at 0x18eecdacfd0>,
  <matplotlib.patches.Wedge at 0x18eecdc4790>,
  <matplotlib.patches.Wedge at 0x18eecdc4eb0>,
  <matplotlib.patches.Wedge at 0x18eecdcf610>,
  <matplotlib.patches.Wedge at 0x18eecdcfd30>,
  <matplotlib.patches.Wedge at 0x18eecddd490>,
  <matplotlib.patches.Wedge at 0x18eecdddbb0>,
  <matplotlib.patches.Wedge at 0x18eecde8310>,
  <matplotlib.patches.Wedge at 0x18eecde8a30>,
  <matplotlib.patches.Wedge at 0x18eec6f7550>,
  <matplotlib.patches.Wedge at 0x18eecdf7880>,
  <matplotlib.patches.Wedge at 0x18eecdf7fa0>,
  <matplotlib.patches.Wedge at 0x18eece04700>,
  <matplotlib.patches.Wedge at 0x18eece04e20>,
  <matplotlib.patches.Wedge at 0x18eece11580>,
  <matplotlib.patches.Wedge at 0x18eece11ca0>,
  <matplotlib.patches.Wedge at 0x18eece1f400>,
  <matplotlib.patches.Wedge at 0x18eece1fb20>,
  <matplotlib.patches.Wedge at 0x18eece2d280>,
  <matplotlib.patches.Wedge at 0x18eece2d9a0>,
  <matplotlib.patches.Wedge at 0x18eece3a100>,
  <matplotlib.patches.Wedge at 0x18eece3a820>,
  <matplotlib.patches.Wedge at 0x18eece3af40>,
  <matplotlib.patches.Wedge at 0x18eece486a0>,
  <matplotlib.patches.Wedge at 0x18eece48dc0>,
  <matplotlib.patches.Wedge at 0x18eece54520>],
 [Text(1.092394307356846, 0.1291304660192809, 'Andhra Pradesh'),
  Text(1.0321783651761125, 0.38027335229064874, 'Assam'),
  Text(0.9150657581747471, 0.6104544685855576, 'Bihar'),
  Text(0.7475120728789119, 0.8069855642452798, 'Chhattisgarh'),
  Text(0.5387533543302139, 0.9590332753340434, 'Delhi'),
  Text(0.3002969884983478, 1.058216291076084, 'Goa'),
  Text(0.04528738179486117, 1.099067356011526, 'Gujarat'),
  Text(-0.2122185967272179, 1.0793346409724516, 'Haryana'),
  Text(-0.4580264707607053, 1.0001058704369719, 'Himachal Pradesh'),
  Text(-0.678586597442501, 0.8657483640015782, 'Jammu & Kashmir'),
  Text(-0.8617410629623605, 0.6836682970596933, 'Jharkhand'),
  Text(-0.997393862030241, 0.46390245093597043, 'Karnataka'),
  Text(-1.0780674189664503, 0.21856495639286794, 'Kerala'),
  Text(-1.0993147733581872, -0.03882047238813081, 'Madhya Pradesh'),
  Text(-1.0599647094137454, -0.2940660041511672, 'Maharashtra'),
  Text(-0.962186316773495, -0.5331017649697436, 'Meghalaya'),
  Text(-0.8113694239985132, -0.7427514105003915, 'Odisha'),
  Text(-0.6158274955897184, -0.9114584442944701, 'Puducherry'),
  Text(-0.3863393697154118, -1.029923245396422, 'Punjab'),
  Text(-0.13555509739265537, -1.0916156904198784, 'Rajasthan'),
  Text(0.12270136501011895, -1.0931351128861673, 'Sikkim'),
  Text(0.37419417233780977, -1.0343977578225998, 'Tamil Nadu'),
  Text(0.6050603117177515, -0.9186413985794553, 'Telangana'),
  Text(0.8025737720321927, -0.7522468613733249, 'Tripura'),
  Text(0.9558470385567217, -0.5443862956415647, 'Uttar Pradesh'),
  Text(1.0527516335591014, -0.31892632070533733, 'Uttarakhand'),
  Text(1.095130309519196, -0.1033905468231516, 'West Bengal')],
 [Text(0.5958514403764614, 0.07043479964688049, '3.75%'),
  Text(0.5630063810051522, 0.207421828522172, '3.75%'),
  Text(0.49912677718622567, 0.3329751646830314, '3.75%'),
  Text(0.4077338579339519, 0.4401739441337889, '3.75%'),
  Text(0.29386546599829844, 0.5231090592731146, '3.75%'),
  Text(0.16379835736273513, 0.5772088860415003, '3.75%'),
  Text(0.024702208251742453, 0.599491285097196, '3.75%'),
  Text(-0.11575559821484611, 0.5887279859849734, '3.75%'),
  Text(-0.24983262041493012, 0.5455122929656209, '3.75%'),
  Text(-0.370138144059546, 0.47222638036449716, '3.75%'),
  Text(-0.4700405797976511, 0.37290998021437816, '3.75%'),
  Text(-0.5440330156528587, 0.2530377005105293, '3.75%'),
  Text(-0.5880367739817002, 0.11921724894156431, '3.75%'),
  Text(-0.5996262400135566, -0.02117480312079862, '3.75%'),
  Text(-0.5781625687711338, -0.16039963862790937, '3.75%'),
  Text(-0.5248289000582699, -0.29078278089258736, '3.75%'),
  Text(-0.44256514036282535, -0.4051371330002135, '3.75%'),
  Text(-0.3359059066853009, -0.4971591514333472, '3.75%'),
  Text(-0.2107305652993155, -0.5617763156707756, '3.75%'),
  Text(-0.07393914403235746, -0.5954267402290245, '3.75%'),
  Text(0.0669280172782467, -0.5962555161197276, '3.75%'),
  Text(0.20410591218425986, -0.5642169588123271, '3.75%'),
  Text(0.3300328973005917, -0.5010771264978846, '3.75%'),
  Text(0.4377675120175596, -0.4103164698399953, '3.75%'),
  Text(0.5213711119400299, -0.2969379794408535, '3.75%'),
  Text(0.5742281637595098, -0.17395981129382035, '3.37%'),
  Text(0.5973438051922886, -0.056394843721719046, '3.00%')])

# Region.1
plt.figure(figsize=(30,8))
sns.countplot(x='Region.1',data=data2)
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>

data2.corr()

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	longitude	latitude
Estimated Unemployment Rate (%)	1.000000	-0.245176	-0.073540	0.149976	-0.023976
Estimated Employed	-0.245176	1.000000	-0.047948	-0.113664	-0.119321
Estimated Labour Participation Rate (%)	-0.073540	-0.047948	1.000000	0.080372	0.397836
longitude	0.149976	-0.113664	0.080372	1.000000	0.125895
latitude	-0.023976	-0.119321	0.397836	0.125895	1.000000

sns.set()
sns.heatmap(data2.corr(),annot = True)

<AxesSubplot:>

# pairplot
sns.pairplot(data2,palette="hls")

<seaborn.axisgrid.PairGrid at 0x18eeced8760>

#Unemployment rate according to different regions of India
data2.columns=['States','Date','Frequency','Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate (%)','Region.1','longitude','latitude']
plt.figure(figsize=(8,6))
sns.histplot(x='Estimated Unemployment Rate',hue='Region.1',data=data2)
plt.show()

import plotly.express as px

Unemployment=data2[["States","Estimated Unemployment Rate","Region.1"]]
figure= px.sunburst(Unemployment,path=['States','Region.1'],
                    values='Estimated Unemployment Rate',
                    width=600,height=600,color_continuous_scale="RdY1Gn",
                    title="Indias Unemployment Rate")
figure.show()

Unemployment is measured by the unemployment rate which is the number of people who are unempolyed as a percentage of the total labour force.we have seen a sharp increase in the unemployment rate during Covid-19,so,analysis of unemployment rate is done in python.